Sequential learning predictor for an image compression system

ABSTRACT

A SYSTEM IS DISCLOSED FOR COMPACTING DIGITAL IMAGE DATA BY MEANS OF PREDICTIVE CODING. THE PREDICTION DECISION RULE IS DEVELOPED BY A HEURISTIC SEQUENTIAL LEARNING PROCESS FROM A SAMPLE IMAGE DATA SET.   A LARGE SET OF TWO DIMENSIONAL IMAGE POINTS REPRESENTED IN BINARY FORM IS PROCESSED IN ORDER TO GENERATE A PREDICTION DECISION TREE STRUCTURE. THE DECISION TREE IS STORED WITHIN THE PREDICTOR MEMORY, THEN, EACH IMAGE POINT TAKEN FROM A SET OF DATA THAT IS TO BE COMPRESSED, IS PREDICTED IN ACCORDANCE WITH THE STORED PREDICTION TREE STRUCTURE. THE PREDICTED VALUE IS THEN ADDED, MODULO-TWO TO THE ACTUAL BINARY VALUE OF THE IMAGE POINT. THE RESULTING OUTPUT OF THE MODULO-TWO ADDITION IS THEN COMPRESSED BY CONVENTIONAL MEANS INTO DATA THAT IS TRANSMITTED TO A RECEIVING UNIT. THE RECEIVING UNIT CONTAINS BOTH A DECODER FOR EXPANDING THE DATA, AND AN INVERSE PREDICTOR FOR CREATING A DATA PATTERN REPRESENTATIVE OF THE IMAGE THAT WAS TRANSMITTED.

DEFENSIVE PUBLICATION UNITED STATES PATENT OFFICE in the application asoriginally filed. The files or these applications are available to thepublic to: inspection and reproduction may be purchased for 30 cents asheet.

Defensive Publication applications have not been examined as to themerits of alleged invention. The Patent 0 09 me! no assertion as to thenovelty of the disclosed subject matter.

PUBLISHED NOVEMBER 5, 1974 Int. Cl. H04n 7/12 U.S. Cl. 235154 10 SheetsDrawing. 26 Pages Specification A system is disclosed for compactingdigital image data by means of predictive coding. The predictiondecision rule is developed by a heuristic sequential learning processfrom a sample image data set.

I! ll omoman SEOUENTML DATA Pnealcros an L A large set of twodimensional image points represented in binary form is processed inorder to generate a prediction decision tree structure. The decisiontree is stored within the predictor memory, then, each image point takenfrom a set of data that is to be compressed, is predicted in accordancewith the stored prediction tree structure. The predicted value is thenadded, modulo-two, to the actual binary value of the image point. Theresulting output of the modulo-two addition is then compressed byconventional means into data that is transmitted to a receiving unit.The receiving unit contains both a decoder for expanding the data, andan inverse predictor for creating a data pattern representative of theimage that was transmitted.

L. R- BAHL ET AL Nov. 5, 1974 SEQUENTIAL LEARNING PREDICTOR FOR AN IMAGECOMPRESSION SYSTEM Original Filed lay 30, 1973 10 Sheets-Sheet 1mokuammm 43:63am Eozwz ow E0085 N q m K ON 2 520 zo E55 $23 205222;; I.h fi 2 r1 "655m 55 MW mwooozm mm iizmnomw Eozwz .EzGEo :2 w QZEZS 2 2 w2 0 n Nov. 5, 1974 L, R BAHL ETAL T928,003

SEQUENTIAL LERNING PREDICTOR FOR AN IMAGE COMPRESSION SYSTEM OriginalFiled lay 30, 973 10 Sheets-Sheet 3 FIG. 2

3 POINT PREDICTOR (PRIOR ART) --DOCUMENT 1* l 16 1? 1s 19 2o 21 22 i a5?e 9101123l 14 s 2 3 4 12 24 13 5 1 2' I 1 T' T k ++++++#+v 2% #:hffk 1wNOV. 5, BAHL ETAL.

SEQUENTIAL LEARNING PREDICTOR FOR AN IMAGE COMPRESSION SYSTEM OriginalFiled Ilay 30, 1973 10 Sheets-Sheet Nov. 5, 1974 R BAHL ETAL T928,003

SEQUENTIAL LEARNING PREDICTOR FOR AN IMAGE COMPRESSION SYSTEM OriginalFiled May 30, 1973 10 Sheets-Sheet 4 FIG. 7 Fl G 7A READ IN USERSPECIFIED H0 7A PARAMETERS AND DATA FIG.

SET TREE LEVEL INDEX T U? SET PREDICTTON TEST POINT INDEX =1 \WT APPLYCURRENT PREDICTION TEST TO ALL DATA AND STORE RESULTS SET NDDE INDEX ATCURRENT TREE LEVEL T0 i COMPUTE PERFDRMANCE OF CURRENT PREDICTIDN TESTFDR CURRENT NODE AND STORE RESULTS IS CURRENT TEST RESULT BETTER THANBEST PREVIOUS PREDICTION TEST Nov. 5, 1974 Original Filed lay 30, 1973L. R. BAHL. E'I'AL T928,003

SEQUENTIAL LEARNING PREDIC'IOR FOR AN IMAGE COMPRESSION SYSTEM 10Sheets-Sheen INCREMENT NUDE NO INDEX BY I SUBSTITUTE CURRENT PREDICTIONTEST FOR BEST PREVIOUS AT CURRENT NODE AND STORE ASSOCIATED PREDICTIONDECISION IN DECISION TREE I26 fiLN NDDES EXAMINED HAVE INCREMENTPREDICTION l0 ALL POINTS TEST POINT INDEX BY I BEEN TESTED MAXIMUM TREELEVEL REACHED INCREMENT TREE LEVEL INDEX BY I ELIMINATE NODES AT CURRENTLEVEL USING COST FUNCTION AND MODIFY DECISION TREE ACCORDINCLY DECREMENTTREE LEVEL INDEX BYI CURRENT TREE LEVEL INDEX =2 NOV. 5, 1914 R BAHLETAL T928,003

SEQUENTIAL LEARNING PREDICTOR FOR AN IMAGE COMPRESSION SYSTEM OriginalFiled llay 30, 1973 10 Sheets-Sheet I:

FIG. L j ST ART Fl (5. 8 A

8A 4 "TEST 1 PROGRAM PROCESS FOR FIG. 2 IDIITESTI}ITEST GENERATING ITREE(SEQ 8B JDIITEST) =1,NTEST DEC TREE) TO BE USED 3 FOR PREDICTIONNROW,NCOL IDATA(I,J),I-1,NROW J -i,NCOL

FIG. 8C

mma

FIG 8D Fl G. 8 III ITIALIZE ARRAY 2 2 IFLAG(,)-0

N1- HHSTO L-IDATAIILJJ) N2-2**(KSTOP1) 2 4 PIS-2* *(KSTOPH) ICOUNTI1F.I,IIII k I l -ICOUNT I1r,I,IIII+I I INITIALIZE ARRAY 208 ICOUNT ,I-o

2| 2 11- mmmsn 22s JJHHJDIITEST) ID =IDATMI ,J) IF =IFLAG(I ,J

YES

I I I N07. 5, 1974 BAHL' ETAL T928,003

SEQUENTIAL LEARNING PHEDICTOR FOR AN IMAGE COMPRIZSSION SYSTEM OriginalFiled lay 30, 1973 10 Sheets-Sheet FIG 9 1 NTEST PROGRAM FOR PREDICTION2 mm JD (ITESU 3 NROW,NCOL 4 IDATA (I,J),I=4,NROW 400 J-4,HCOL

5 ITREE (lTADLITAD-LNS-i II=I+ID (ITEST) 422 JJ=J+JD(ITEST) FHA L=0 YESM NO L=IDATA(II,JJ)

YES ITAD- 2*ITAD+L DONE

